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Open AccessArticle

State of Health Estimation of Li-ion Batteries with Regeneration Phenomena: A Similar Rest Time-Based Prognostic Framework

1
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
2
IVHM Centre, Cranfield University, Cranfield MK43 0AL, UK
3
Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Academic Editor: Ka Lok Man
Symmetry 2017, 9(1), 4; https://doi.org/10.3390/sym9010004
Received: 31 October 2016 / Revised: 17 December 2016 / Accepted: 21 December 2016 / Published: 24 December 2016
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
State of health (SOH) prediction in Li-ion batteries plays an important role in intelligent battery management systems (BMS). However, the existence of capacity regeneration phenomena remains a great challenge for accurately predicting the battery SOH. This paper proposes a novel prognostic framework to predict the regeneration phenomena of the current battery using the data of a historical battery. The global degradation trend and regeneration phenomena (characterized by regeneration amplitude and regeneration cycle number) of the current battery are extracted from its raw SOH time series. Moreover, regeneration information of the historical battery derived from corresponding raw SOH data is utilized in this framework. The global degradation trend and regeneration phenomena of the current battery are predicted, and then the prediction results are integrated together to calculate the overall SOH prediction values. Particle swarm optimization (PSO) is employed to obtain an appropriate regeneration threshold for the historical battery. Gaussian process (GP) model is adopted to predict the global degradation trend, and linear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated using experimental data from the degradation tests of Li-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framework. View Full-Text
Keywords: Li-ion batteries; state of health; regeneration phenomena; particle swarm optimization; Gaussian process Li-ion batteries; state of health; regeneration phenomena; particle swarm optimization; Gaussian process
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Qin, T.; Zeng, S.; Guo, J.; Skaf, Z. State of Health Estimation of Li-ion Batteries with Regeneration Phenomena: A Similar Rest Time-Based Prognostic Framework. Symmetry 2017, 9, 4.

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